Is the RTX PRO 6000 S suitable for large language model inference and fine-tuning?

Yes, the 96 GB VRAM capacity and about 1396 GB/s memory bandwidth make it well-suited for hosting and fine-tuning large language models that exceed the memory limits of standard consumer cards. You can deploy quickly on Cloudj using ready templates for vLLM, Ollama, or PyTorch.

How does this GPU compare to smaller workstation or larger datacenter GPUs?

As a Blackwell workstation GPU with 96 GB of VRAM, it provides substantially more memory than consumer GPUs and previous-generation workstation cards, allowing larger batch sizes and unquantized weights. Full datacenter GPUs generally offer higher memory bandwidth for massive scale-out training, while smaller GPUs are preferable if your workload does not utilize the full 96 GB capacity.

What software templates and access methods are included on Cloudj?

Every instance includes direct SSH access so you can configure your environment as needed. Cloudj also provides ready-to-use templates for PyTorch with CUDA, vLLM, Ollama, and ComfyUI to support compute capability 12.0 workflows.

How does billing work for the RTX PRO 6000 S?

Rentals on Cloudj use per-hour billing. You only pay for the time your instance is active, making it practical for both short testing sessions and extended workstation compute runs.

GPU Cloud · NVIDIA · Blackwell

Rent NVIDIA RTX PRO 6000 S GPU by the hour

The NVIDIA RTX PRO 6000 S is a Blackwell-architecture workstation GPU equipped with 96 GB of VRAM and compute capability 12.0. It delivers about 93.6 TFLOPS of standard floating-point compute and a memory bandwidth of about 1396 GB/s within a 600 W power envelope. On Cloudj, you can rent this high-memory workstation card with per-hour billing and full SSH access.

Rent RTX PRO 6000 S All GPUs

Specifications

VRAM per GPU

96 GB

Architecture

NVIDIA Blackwell

Compute capability

FP32 compute

≈ 93.6 TFLOPS

Memory bandwidth

≈ 1396 GB/s

Power limit

600 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Available configurations

Live stock and prices, updated continuously. The final price is confirmed before you order.

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What the RTX PRO 6000 S is good for

Large Language Model Inference

Run large models comfortably within the 96 GB VRAM pool using pre-configured vLLM or Ollama templates.

Deep Learning Fine-Tuning

Train and adapt complex neural networks in PyTorch with about 1396 GB/s of memory bandwidth and compute capability 12.0.

High-Resolution Generative Media

Generate detailed images and video in ComfyUI while keeping multiple heavy checkpoints loaded in memory simultaneously.

Workstation Graphics and Simulation

Execute demanding workstation compute tasks and spatial simulations using about 93.6 TFLOPS of single-precision performance.

Is it the right GPU for you?

Choose the RTX PRO 6000 S when your workloads require 96 GB of memory and Blackwell architecture features without moving to a datacenter-segment GPU. If your models fit into a smaller memory footprint, a consumer or lower-tier workstation card is more economical, whereas massive multi-node training jobs may warrant a dedicated datacenter GPU with higher memory bandwidth.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX PRO 6000 S	96 GB	≈ 93.6 TFLOPS	600 W
NVIDIA RTX PRO 6000 Max-Q	96 GB	≈ 119 TFLOPS	300 W	Details →
NVIDIA RTX PRO 6000 WS	96 GB	≈ 119 TFLOPS	600 W	Details →
NVIDIA RTX 6000D	84 GB	≈ 77.6 TFLOPS	600 W	Details →
NVIDIA RTX 6000Ada	48 GB	≈ 81.4 TFLOPS	300 W	Details →

Frequently asked questions

Rent the RTX PRO 6000 S by the hour

Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.

Rent RTX PRO 6000 S All GPUs